Triple
T34000220
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | SS Empress of England |
E871793
|
entity |
| Predicate | passengerClassLayout |
P48161
|
FINISHED |
| Object | multiple passenger classes |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: multiple passenger classes | Statement: [SS Empress of England, passengerClassLayout, multiple passenger classes]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: passengerClassLayout Context triple: [SS Empress of England, passengerClassLayout, multiple passenger classes]
-
A.
seatClass
Indicates the travel or seating category assigned to a passenger or seat (e.g., economy, business, first class).
-
B.
passengerLevel
Indicates the relative status, class, or priority assigned to a passenger within a transportation or service context.
-
C.
aircraftSeatingCategory
Indicates the classification of an aircraft’s seating arrangement or capacity type associated with an entity.
-
D.
classesOfSeats
chosen
Indicates the different categories or types of seats associated with something, such as a venue, vehicle, or event.
-
E.
passengerConfiguration
Indicates how passengers are arranged, seated, or distributed within a vehicle or transport setting.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69f3499f8cbc81908de6ec89fa91ea8f |
completed | April 30, 2026, 12:22 p.m. |
| NER | Named-entity recognition | batch_69f760a35b988190904e6267553ad2fe |
completed | May 3, 2026, 2:50 p.m. |
| PD | Predicate disambiguation | batch_69f75eb3d6f081908c933474eb359e3d |
completed | May 3, 2026, 2:41 p.m. |
Created at: May 1, 2026, 1:50 a.m.